{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [
    {
     "data": {
      "text/plain": "                                       快递单号 发件人姓名        发件人电话  \\\n0      38a997a8-8cb3-4830-bab1-78ecc090acbb   葛建国  18756819476   \n1      45ad4cbb-01c4-4b18-b57a-21e57043724f    武博  18110821822   \n2      b1a60faa-a67f-44d8-b6bf-4139c44ea28c    陈慧  13235779800   \n3      4dbbb049-19be-497c-9b2a-23a669e95112    李洁  15932391745   \n4      e3cffe80-234d-4a64-9d89-b8e0af55af9f   徐秀珍  15757446653   \n...                                     ...   ...          ...   \n49995  8b8f2420-4f47-49f2-8b0a-25f4ad8836fe   张雪梅  13189469654   \n49996  26bb7218-12bd-4948-a3eb-bedd4a181c4f   陆建平  18004267400   \n49997  4d67dd0c-7e5f-4681-b99d-5801c51223ed   欧淑珍  14568795658   \n49998  c749e39b-73bb-404c-8e49-361758f155aa    陈楠  15615691069   \n49999  b5929c14-672d-4d5d-b59b-7e736a0e1e08    崔静  15939740857   \n\n                         发件人地址 收件人姓名        收件人电话                     收件人地址  \\\n0          重庆市斌市永川汕尾路U座 586857   李淑华  13549725098       四川省柳州县花溪廖街i座 756878   \n1          安徽省霞市白云关岭路U座 693653   徐凤兰  13525593467       吉林省巢湖县东丽邢路v座 555342   \n2           重庆市刚市滨城聂路v座 274977    马娜  15716930739      广东省巢湖市兴山西宁路x座 452256   \n3        贵州省太原市门头沟太原街E座 705965   董凤英  18838705200  新疆维吾尔自治区红霞市梁平王路a座 588896   \n4       黑龙江省上海市淄川石家庄路t座 657983   李淑兰  13271743931       青海省桂芳市山亭张街m座 179595   \n...                        ...   ...          ...                       ...   \n49995   山东省齐齐哈尔县高坪北镇街W座 671173    曾林  13030320134       甘肃省欢县和平荆门街U座 224287   \n49996  广西壮族自治区帅市秀英嘉禾街U座 828485    范波  15557708068      福建省昆明市沈北新白路A座 250271   \n49997    吉林省潜江市华龙张家港街M座 534498    廖娟  13726791059       山西省雷县金平长沙路X座 765057   \n49998     台湾省海门市海陵西宁街U座 201583   甄雪梅  18656113978    内蒙古自治区成都县高坪汪路O座 779588   \n49999    山东省邯郸县平山哈尔滨街f座 729703    李颖  18284318854      贵州省建平市平山长沙路r座 274811   \n\n      货物名称  货物重量(kg) 运输状态  \n0       帮助     19.17  已揽件  \n1       只有     11.03  已签收  \n2       内容     15.63  运输中  \n3       设备     10.54  派送中  \n4       信息      2.09  运输中  \n...    ...       ...  ...  \n49995   世界      9.59  派送中  \n49996   大家      4.61  派送中  \n49997   社区      5.74  已揽件  \n49998   表示     18.44  已签收  \n49999   当然     17.44  已揽件  \n\n[50000 rows x 10 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>快递单号</th>\n      <th>发件人姓名</th>\n      <th>发件人电话</th>\n      <th>发件人地址</th>\n      <th>收件人姓名</th>\n      <th>收件人电话</th>\n      <th>收件人地址</th>\n      <th>货物名称</th>\n      <th>货物重量(kg)</th>\n      <th>运输状态</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>38a997a8-8cb3-4830-bab1-78ecc090acbb</td>\n      <td>葛建国</td>\n      <td>18756819476</td>\n      <td>重庆市斌市永川汕尾路U座 586857</td>\n      <td>李淑华</td>\n      <td>13549725098</td>\n      <td>四川省柳州县花溪廖街i座 756878</td>\n      <td>帮助</td>\n      <td>19.17</td>\n      <td>已揽件</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>45ad4cbb-01c4-4b18-b57a-21e57043724f</td>\n      <td>武博</td>\n      <td>18110821822</td>\n      <td>安徽省霞市白云关岭路U座 693653</td>\n      <td>徐凤兰</td>\n      <td>13525593467</td>\n      <td>吉林省巢湖县东丽邢路v座 555342</td>\n      <td>只有</td>\n      <td>11.03</td>\n      <td>已签收</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>b1a60faa-a67f-44d8-b6bf-4139c44ea28c</td>\n      <td>陈慧</td>\n      <td>13235779800</td>\n      <td>重庆市刚市滨城聂路v座 274977</td>\n      <td>马娜</td>\n      <td>15716930739</td>\n      <td>广东省巢湖市兴山西宁路x座 452256</td>\n      <td>内容</td>\n      <td>15.63</td>\n      <td>运输中</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4dbbb049-19be-497c-9b2a-23a669e95112</td>\n      <td>李洁</td>\n      <td>15932391745</td>\n      <td>贵州省太原市门头沟太原街E座 705965</td>\n      <td>董凤英</td>\n      <td>18838705200</td>\n      <td>新疆维吾尔自治区红霞市梁平王路a座 588896</td>\n      <td>设备</td>\n      <td>10.54</td>\n      <td>派送中</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>e3cffe80-234d-4a64-9d89-b8e0af55af9f</td>\n      <td>徐秀珍</td>\n      <td>15757446653</td>\n      <td>黑龙江省上海市淄川石家庄路t座 657983</td>\n      <td>李淑兰</td>\n      <td>13271743931</td>\n      <td>青海省桂芳市山亭张街m座 179595</td>\n      <td>信息</td>\n      <td>2.09</td>\n      <td>运输中</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>49995</th>\n      <td>8b8f2420-4f47-49f2-8b0a-25f4ad8836fe</td>\n      <td>张雪梅</td>\n      <td>13189469654</td>\n      <td>山东省齐齐哈尔县高坪北镇街W座 671173</td>\n      <td>曾林</td>\n      <td>13030320134</td>\n      <td>甘肃省欢县和平荆门街U座 224287</td>\n      <td>世界</td>\n      <td>9.59</td>\n      <td>派送中</td>\n    </tr>\n    <tr>\n      <th>49996</th>\n      <td>26bb7218-12bd-4948-a3eb-bedd4a181c4f</td>\n      <td>陆建平</td>\n      <td>18004267400</td>\n      <td>广西壮族自治区帅市秀英嘉禾街U座 828485</td>\n      <td>范波</td>\n      <td>15557708068</td>\n      <td>福建省昆明市沈北新白路A座 250271</td>\n      <td>大家</td>\n      <td>4.61</td>\n      <td>派送中</td>\n    </tr>\n    <tr>\n      <th>49997</th>\n      <td>4d67dd0c-7e5f-4681-b99d-5801c51223ed</td>\n      <td>欧淑珍</td>\n      <td>14568795658</td>\n      <td>吉林省潜江市华龙张家港街M座 534498</td>\n      <td>廖娟</td>\n      <td>13726791059</td>\n      <td>山西省雷县金平长沙路X座 765057</td>\n      <td>社区</td>\n      <td>5.74</td>\n      <td>已揽件</td>\n    </tr>\n    <tr>\n      <th>49998</th>\n      <td>c749e39b-73bb-404c-8e49-361758f155aa</td>\n      <td>陈楠</td>\n      <td>15615691069</td>\n      <td>台湾省海门市海陵西宁街U座 201583</td>\n      <td>甄雪梅</td>\n      <td>18656113978</td>\n      <td>内蒙古自治区成都县高坪汪路O座 779588</td>\n      <td>表示</td>\n      <td>18.44</td>\n      <td>已签收</td>\n    </tr>\n    <tr>\n      <th>49999</th>\n      <td>b5929c14-672d-4d5d-b59b-7e736a0e1e08</td>\n      <td>崔静</td>\n      <td>15939740857</td>\n      <td>山东省邯郸县平山哈尔滨街f座 729703</td>\n      <td>李颖</td>\n      <td>18284318854</td>\n      <td>贵州省建平市平山长沙路r座 274811</td>\n      <td>当然</td>\n      <td>17.44</td>\n      <td>已揽件</td>\n    </tr>\n  </tbody>\n</table>\n<p>50000 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00008\\\\02-02-5-00008\\logistics_data.csv\")\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['9f0c8084-2798-4c04-b1ed-c02f4b995faf',\n       'ff469502-a4f8-45aa-a89f-4f42b7e14727',\n       '65844178-838b-47f4-af9e-96026dc0c316',\n       'd330aac8-8ebc-44a0-93f4-50b7dc14f1c4',\n       'edbad98c-d828-4874-bcf0-76e841751279',\n       'dc1699ab-5bd6-4384-b833-c9d863651abd',\n       '2699f7b3-3090-49c8-8e52-e904d5491404',\n       'd2456889-38fa-4482-97bd-ec964161b45b',\n       '51b48c12-67ec-45a8-ab3f-28fc535cf946',\n       '7faaeb95-def5-40a8-b0cd-2e75fcda6ba7',\n       '1deb97bc-55b6-4083-9a78-5a2bcf2e7af3',\n       'dddfabb9-db58-4822-9264-6d0e3a412bc4',\n       '70cb566b-01cc-4bed-81e0-8e8ce905d22b',\n       'ac96817a-9350-4205-ab5f-caff49a394a2',\n       '1bf2b484-3249-4921-80c6-c3555bc66249',\n       '4d1f4916-23fa-4314-adf7-ba31fc47512d'], dtype=object)"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# w = df[\"货物重量(kg)\"].idxmax()\n",
    "# df.loc[w,\"快递单号\"]\n",
    "df[df['货物重量(kg)'] == df[\"货物重量(kg)\"].max()][\"快递单号\"].values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "data": {
      "text/plain": "10.00329"
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"运输状态\"].value_counts()\n",
    "df[\"货物重量(kg)\"].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "1        45ad4cbb-01c4-4b18-b57a-21e57043724f\n5        7523df77-df9e-485c-9b4a-0991d17975d4\n12       1bc36020-38da-4795-8c71-35eb1472c059\n16       5b19d7e3-e337-4e26-a4a4-29beaa5dd8bd\n20       d92eda77-5751-4e3c-b372-636bf911465c\n                         ...                 \n49975    46bfd400-749d-4d78-b9ec-220858c3abc1\n49978    a7e28e71-673d-4c98-bf8d-8f0124ef2f4f\n49990    688d4f64-fa8e-4233-bce9-c2620d07fa58\n49994    0f8ab3e2-8073-4500-afd3-3916ad19341e\n49998    c749e39b-73bb-404c-8e49-361758f155aa\nName: 快递单号, Length: 12576, dtype: object"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('运输状态 == \"已签收\"')[\"快递单号\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "city\n上海市          727\n上海市上海市         5\n上海市东市          2\n上海市东市沙市        1\n上海市东莞市         2\n            ... \n黑龙江省鹏县         2\n黑龙江省齐齐哈尔县      3\n黑龙江省齐齐哈尔市      5\n黑龙江省龙县         6\n黑龙江省龙市         3\nName: 快递单号, Length: 12768, dtype: int64"
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pattern = r'([\\u4e00-\\u9fa5]+市|[\\u4e00-\\u9fa5]+县)'\n",
    "df[\"city\"] = df['收件人地址'].str.extract(pattern)\n",
    "rs = df.groupby('city')['快递单号'].count()\n",
    "rs"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "           订单编号    订单金额        订单日期  客户类型 客户来源渠道 客户地区 产品类别  配送时间 是否退货\n0      ORD00001  815.51  2023-09-02  普通客户   线上渠道   西安   电脑     8    是\n1      ORD00002  651.16  2022-05-13  普通客户   线下渠道   武汉   图书     6    是\n2      ORD00003  745.78  2021-11-24  普通客户   线下渠道   广州   玩具     4    是\n3      ORD00004  449.52  2021-09-13   VIP   线下渠道   上海   图书     4    是\n4      ORD00005  625.20  2023-08-14  普通客户   线下渠道   深圳   家电     6    否\n...         ...     ...         ...   ...    ...  ...  ...   ...  ...\n49995  ORD49996  951.09  2019-09-15   VIP   线上渠道   成都   家具     7    是\n49996  ORD49997  583.95  2020-09-28   VIP   线上渠道   南京  化妆品     3    否\n49997  ORD49998  123.57  2023-03-17  普通客户   线上渠道   南京   家电     4    是\n49998  ORD49999  464.08  2022-06-11  普通客户   线上渠道   成都  化妆品     8    否\n49999  ORD50000  830.06  2019-04-08  普通客户   线上渠道   广州   食品     1    否\n\n[50000 rows x 9 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>订单编号</th>\n      <th>订单金额</th>\n      <th>订单日期</th>\n      <th>客户类型</th>\n      <th>客户来源渠道</th>\n      <th>客户地区</th>\n      <th>产品类别</th>\n      <th>配送时间</th>\n      <th>是否退货</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ORD00001</td>\n      <td>815.51</td>\n      <td>2023-09-02</td>\n      <td>普通客户</td>\n      <td>线上渠道</td>\n      <td>西安</td>\n      <td>电脑</td>\n      <td>8</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ORD00002</td>\n      <td>651.16</td>\n      <td>2022-05-13</td>\n      <td>普通客户</td>\n      <td>线下渠道</td>\n      <td>武汉</td>\n      <td>图书</td>\n      <td>6</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ORD00003</td>\n      <td>745.78</td>\n      <td>2021-11-24</td>\n      <td>普通客户</td>\n      <td>线下渠道</td>\n      <td>广州</td>\n      <td>玩具</td>\n      <td>4</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ORD00004</td>\n      <td>449.52</td>\n      <td>2021-09-13</td>\n      <td>VIP</td>\n      <td>线下渠道</td>\n      <td>上海</td>\n      <td>图书</td>\n      <td>4</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ORD00005</td>\n      <td>625.20</td>\n      <td>2023-08-14</td>\n      <td>普通客户</td>\n      <td>线下渠道</td>\n      <td>深圳</td>\n      <td>家电</td>\n      <td>6</td>\n      <td>否</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>49995</th>\n      <td>ORD49996</td>\n      <td>951.09</td>\n      <td>2019-09-15</td>\n      <td>VIP</td>\n      <td>线上渠道</td>\n      <td>成都</td>\n      <td>家具</td>\n      <td>7</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>49996</th>\n      <td>ORD49997</td>\n      <td>583.95</td>\n      <td>2020-09-28</td>\n      <td>VIP</td>\n      <td>线上渠道</td>\n      <td>南京</td>\n      <td>化妆品</td>\n      <td>3</td>\n      <td>否</td>\n    </tr>\n    <tr>\n      <th>49997</th>\n      <td>ORD49998</td>\n      <td>123.57</td>\n      <td>2023-03-17</td>\n      <td>普通客户</td>\n      <td>线上渠道</td>\n      <td>南京</td>\n      <td>家电</td>\n      <td>4</td>\n      <td>是</td>\n    </tr>\n    <tr>\n      <th>49998</th>\n      <td>ORD49999</td>\n      <td>464.08</td>\n      <td>2022-06-11</td>\n      <td>普通客户</td>\n      <td>线上渠道</td>\n      <td>成都</td>\n      <td>化妆品</td>\n      <td>8</td>\n      <td>否</td>\n    </tr>\n    <tr>\n      <th>49999</th>\n      <td>ORD50000</td>\n      <td>830.06</td>\n      <td>2019-04-08</td>\n      <td>普通客户</td>\n      <td>线上渠道</td>\n      <td>广州</td>\n      <td>食品</td>\n      <td>1</td>\n      <td>否</td>\n    </tr>\n  </tbody>\n</table>\n<p>50000 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00008\\\\02-02-5-00008\\orders.csv\")\n",
    "df\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.random.randint(1,100,size=50000)\n",
    "df[\"订单数量\"] = arr\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(df['订单数量'][:500], df['订单金额'][:500])\n",
    "plt.title('订单数量与订单金额的相关性')\n",
    "plt.xlabel('订单数量')\n",
    "plt.ylabel('订单金额')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x1b4b0c72c70>,\n  <matplotlib.lines.Line2D at 0x1b4b0c72f40>],\n 'caps': [<matplotlib.lines.Line2D at 0x1b4b0c81250>,\n  <matplotlib.lines.Line2D at 0x1b4b0c814c0>],\n 'boxes': [<matplotlib.lines.Line2D at 0x1b4b0c729a0>],\n 'medians': [<matplotlib.lines.Line2D at 0x1b4b0c81790>],\n 'fliers': [<matplotlib.lines.Line2D at 0x1b4b0c81a60>],\n 'means': []}"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = df.query(\"客户类型 ==  'VIP' \")[\"订单金额\"]\n",
    "n = df.query(\"客户类型 ==  '普通客户' \")[\"订单金额\"]\n",
    "\n",
    "plt.boxplot(v)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x1b4b0cdb730>,\n  <matplotlib.lines.Line2D at 0x1b4b0cdba00>],\n 'caps': [<matplotlib.lines.Line2D at 0x1b4b0cdbcd0>,\n  <matplotlib.lines.Line2D at 0x1b4b0cdbfa0>],\n 'boxes': [<matplotlib.lines.Line2D at 0x1b4b0cdb460>],\n 'medians': [<matplotlib.lines.Line2D at 0x1b4b0cea2b0>],\n 'fliers': [<matplotlib.lines.Line2D at 0x1b4b0cea580>],\n 'means': []}"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(n)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['图书', '手机', '家电', '家具', '食品'], dtype='object', name='产品类别')"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category = df.groupby(\"产品类别\")[\"订单金额\"].sum().sort_values(ascending=False)[:5].index\n",
    "category"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_12096\\1219215025.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[\"订单日期\"] = pd.to_datetime(df1[\"订单日期\"])\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_12096\\1219215025.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1[\"month\"] = df1[\"订单日期\"].dt.month\n"
     ]
    }
   ],
   "source": [
    "df1 = df.query(\"产品类别 in @ category\")\n",
    "df1[\"订单日期\"] = pd.to_datetime(df1[\"订单日期\"])\n",
    "df1[\"month\"] = df1[\"订单日期\"].dt.month\n",
    "result = df1.groupby([\"产品类别\",\"month\"])[\"订单金额\"].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s = \"\"\n",
    "h = []\n",
    "m = []\n",
    "for index,data in enumerate(result.index):\n",
    "\th.append(data[1])\n",
    "\tm.append(result.loc[data])\n",
    "\tif (index+1) % 12 == 0:\n",
    "\t\tplt.plot(h,m,label=data[0])\n",
    "\t\th.clear()\n",
    "\t\tm.clear()\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 2 artists>"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result2 = df.groupby(\"客户来源渠道\")[\"订单金额\"].sum()\n",
    "plt.bar(result2.index,result2.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 10 artists>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result3 = df.groupby(\"客户地区\")[\"订单金额\"].sum()\n",
    "plt.bar(result3.index,result3.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x1b4b7a1b3a0>,\n  <matplotlib.lines.Line2D at 0x1b4b7a1b670>],\n 'caps': [<matplotlib.lines.Line2D at 0x1b4b7a1b970>,\n  <matplotlib.lines.Line2D at 0x1b4b7a1bc40>],\n 'boxes': [<matplotlib.lines.Line2D at 0x1b4b7a1b0d0>],\n 'medians': [<matplotlib.lines.Line2D at 0x1b4b7a1bf10>],\n 'fliers': [<matplotlib.lines.Line2D at 0x1b4b7a27220>],\n 'means': []}"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result4 = df.groupby(\"客户地区\")[\"订单编号\"].count()\n",
    "plt.boxplot(result4.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "      订单编号\n产品类别      \n化妆品   5044\n图书    5095\n家具    5015\n家电    5081\n手机    5060\n服装    4892\n玩具    4845\n电脑    4949\n电视    5004\n食品    5015",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>订单编号</th>\n    </tr>\n    <tr>\n      <th>产品类别</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>化妆品</th>\n      <td>5044</td>\n    </tr>\n    <tr>\n      <th>图书</th>\n      <td>5095</td>\n    </tr>\n    <tr>\n      <th>家具</th>\n      <td>5015</td>\n    </tr>\n    <tr>\n      <th>家电</th>\n      <td>5081</td>\n    </tr>\n    <tr>\n      <th>手机</th>\n      <td>5060</td>\n    </tr>\n    <tr>\n      <th>服装</th>\n      <td>4892</td>\n    </tr>\n    <tr>\n      <th>玩具</th>\n      <td>4845</td>\n    </tr>\n    <tr>\n      <th>电脑</th>\n      <td>4949</td>\n    </tr>\n    <tr>\n      <th>电视</th>\n      <td>5004</td>\n    </tr>\n    <tr>\n      <th>食品</th>\n      <td>5015</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res5 = df.groupby(\"产品类别\")[[\"订单编号\"]].count()\n",
    "res5"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "outputs": [
    {
     "data": {
      "text/plain": "      订单编号\n产品类别      \n化妆品   2508\n图书    2523\n家具    2483\n家电    2566\n手机    2519\n服装    2435\n玩具    2392\n电脑    2499\n电视    2493\n食品    2549",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>订单编号</th>\n    </tr>\n    <tr>\n      <th>产品类别</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>化妆品</th>\n      <td>2508</td>\n    </tr>\n    <tr>\n      <th>图书</th>\n      <td>2523</td>\n    </tr>\n    <tr>\n      <th>家具</th>\n      <td>2483</td>\n    </tr>\n    <tr>\n      <th>家电</th>\n      <td>2566</td>\n    </tr>\n    <tr>\n      <th>手机</th>\n      <td>2519</td>\n    </tr>\n    <tr>\n      <th>服装</th>\n      <td>2435</td>\n    </tr>\n    <tr>\n      <th>玩具</th>\n      <td>2392</td>\n    </tr>\n    <tr>\n      <th>电脑</th>\n      <td>2499</td>\n    </tr>\n    <tr>\n      <th>电视</th>\n      <td>2493</td>\n    </tr>\n    <tr>\n      <th>食品</th>\n      <td>2549</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res6 = df.query('是否退货 == \"是\"').groupby(\"产品类别\")[[\"订单编号\"]].count()\n",
    "res6"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "data": {
      "text/plain": "      订单编号_x  订单编号_y       退货率\n产品类别                          \n化妆品     5044    2508  0.497224\n图书      5095    2523  0.495191\n家具      5015    2483  0.495115\n家电      5081    2566  0.505019\n手机      5060    2519  0.497826\n服装      4892    2435  0.497751\n玩具      4845    2392  0.493705\n电脑      4949    2499  0.504950\n电视      5004    2493  0.498201\n食品      5015    2549  0.508275",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>订单编号_x</th>\n      <th>订单编号_y</th>\n      <th>退货率</th>\n    </tr>\n    <tr>\n      <th>产品类别</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>化妆品</th>\n      <td>5044</td>\n      <td>2508</td>\n      <td>0.497224</td>\n    </tr>\n    <tr>\n      <th>图书</th>\n      <td>5095</td>\n      <td>2523</td>\n      <td>0.495191</td>\n    </tr>\n    <tr>\n      <th>家具</th>\n      <td>5015</td>\n      <td>2483</td>\n      <td>0.495115</td>\n    </tr>\n    <tr>\n      <th>家电</th>\n      <td>5081</td>\n      <td>2566</td>\n      <td>0.505019</td>\n    </tr>\n    <tr>\n      <th>手机</th>\n      <td>5060</td>\n      <td>2519</td>\n      <td>0.497826</td>\n    </tr>\n    <tr>\n      <th>服装</th>\n      <td>4892</td>\n      <td>2435</td>\n      <td>0.497751</td>\n    </tr>\n    <tr>\n      <th>玩具</th>\n      <td>4845</td>\n      <td>2392</td>\n      <td>0.493705</td>\n    </tr>\n    <tr>\n      <th>电脑</th>\n      <td>4949</td>\n      <td>2499</td>\n      <td>0.504950</td>\n    </tr>\n    <tr>\n      <th>电视</th>\n      <td>5004</td>\n      <td>2493</td>\n      <td>0.498201</td>\n    </tr>\n    <tr>\n      <th>食品</th>\n      <td>5015</td>\n      <td>2549</td>\n      <td>0.508275</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res7 = res5.merge(res6,left_index=True,right_index=True)\n",
    "res7[\"退货率\"] = res7[\"订单编号_y\"]/res7[\"订单编号_x\"]\n",
    "res7"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 10 artists>"
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(res7.index,res7[\"退货率\"])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "(array([5019., 5010., 4975., 4923., 5007., 5092., 5094., 5132., 4821.,\n        4927.]),\n array([ 1. ,  1.9,  2.8,  3.7,  4.6,  5.5,  6.4,  7.3,  8.2,  9.1, 10. ]),\n <BarContainer object of 10 artists>)"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"配送时间\"].mean()\n",
    "plt.hist(df[\"配送时间\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "产品类别\n化妆品    2733928.66\n图书     2826720.27\n家具     2774931.57\n家电     2783952.94\n手机     2786553.43\n服装     2679784.21\n玩具     2679752.42\n电脑     2708859.82\n电视     2758229.52\n食品     2765235.21\nName: 订单金额, dtype: float64"
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res8 = df.groupby(\"产品类别\")[\"订单金额\"].sum()\n",
    "res8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(res8.values,labels=res8.index,autopct='%.1f%%')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "              日期     收盘价     最高价     最低价     开盘价     成交量       市值   涨跌幅   换手率  \\\n0     2026-01-15   94.53  104.17   92.13   98.65  190228  1759489  0.06  0.13   \n1     2024-10-21  103.14  107.63   98.66  101.41  125858  1979626  0.07  0.18   \n2     2026-02-23  118.96  123.81  110.71  111.84  123261  1747556 -0.02  0.06   \n3     2025-08-26   94.90   98.46   88.06   97.48  116065  2087308 -0.06  0.05   \n4     2024-07-23  102.80  110.39   96.88  102.59  109950  2120478 -0.09  0.18   \n...          ...     ...     ...     ...     ...     ...      ...   ...   ...   \n4995  2024-09-06   81.01   83.46   72.61   75.25   80381  2064695 -0.10  0.09   \n4996  2026-04-28  113.68  116.59  111.07  115.94   95991  1579650  0.06  0.06   \n4997  2026-04-12   91.75   96.90   89.26   92.13  186571  1550355 -0.01  0.13   \n4998  2024-11-26  112.27  114.89  104.20  107.91  182485   930311  0.09  0.09   \n4999  2025-07-19   93.60  102.36   84.24   91.71  136610  1989741  0.07  0.11   \n\n      股票代码  行业  \n0     A001  科技  \n1     A001  金融  \n2     A001  金融  \n3     A002  金融  \n4     A001  金融  \n...    ...  ..  \n4995  A001  金融  \n4996  A001  科技  \n4997  A002  金融  \n4998  A002  科技  \n4999  A002  金融  \n\n[5000 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>日期</th>\n      <th>收盘价</th>\n      <th>最高价</th>\n      <th>最低价</th>\n      <th>开盘价</th>\n      <th>成交量</th>\n      <th>市值</th>\n      <th>涨跌幅</th>\n      <th>换手率</th>\n      <th>股票代码</th>\n      <th>行业</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2026-01-15</td>\n      <td>94.53</td>\n      <td>104.17</td>\n      <td>92.13</td>\n      <td>98.65</td>\n      <td>190228</td>\n      <td>1759489</td>\n      <td>0.06</td>\n      <td>0.13</td>\n      <td>A001</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2024-10-21</td>\n      <td>103.14</td>\n      <td>107.63</td>\n      <td>98.66</td>\n      <td>101.41</td>\n      <td>125858</td>\n      <td>1979626</td>\n      <td>0.07</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2026-02-23</td>\n      <td>118.96</td>\n      <td>123.81</td>\n      <td>110.71</td>\n      <td>111.84</td>\n      <td>123261</td>\n      <td>1747556</td>\n      <td>-0.02</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2025-08-26</td>\n      <td>94.90</td>\n      <td>98.46</td>\n      <td>88.06</td>\n      <td>97.48</td>\n      <td>116065</td>\n      <td>2087308</td>\n      <td>-0.06</td>\n      <td>0.05</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2024-07-23</td>\n      <td>102.80</td>\n      <td>110.39</td>\n      <td>96.88</td>\n      <td>102.59</td>\n      <td>109950</td>\n      <td>2120478</td>\n      <td>-0.09</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4995</th>\n      <td>2024-09-06</td>\n      <td>81.01</td>\n      <td>83.46</td>\n      <td>72.61</td>\n      <td>75.25</td>\n      <td>80381</td>\n      <td>2064695</td>\n      <td>-0.10</td>\n      <td>0.09</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4996</th>\n      <td>2026-04-28</td>\n      <td>113.68</td>\n      <td>116.59</td>\n      <td>111.07</td>\n      <td>115.94</td>\n      <td>95991</td>\n      <td>1579650</td>\n      <td>0.06</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>4997</th>\n      <td>2026-04-12</td>\n      <td>91.75</td>\n      <td>96.90</td>\n      <td>89.26</td>\n      <td>92.13</td>\n      <td>186571</td>\n      <td>1550355</td>\n      <td>-0.01</td>\n      <td>0.13</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4998</th>\n      <td>2024-11-26</td>\n      <td>112.27</td>\n      <td>114.89</td>\n      <td>104.20</td>\n      <td>107.91</td>\n      <td>182485</td>\n      <td>930311</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>A002</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>4999</th>\n      <td>2025-07-19</td>\n      <td>93.60</td>\n      <td>102.36</td>\n      <td>84.24</td>\n      <td>91.71</td>\n      <td>136610</td>\n      <td>1989741</td>\n      <td>0.07</td>\n      <td>0.11</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n  </tbody>\n</table>\n<p>5000 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00008\\\\02-02-5-00008\\金融数据.csv\")\n",
    "df\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "               收盘价          最高价          最低价          开盘价            成交量  \\\ncount  5000.000000  5000.000000  5000.000000  5000.000000    5000.000000   \nmean     99.864676   104.904968    94.891296    99.882582  140136.678200   \nstd      11.578104    11.968002    11.978240    12.262939   34672.984375   \nmin      80.000000    80.710000    70.140000    71.510000   80021.000000   \n25%      89.750000    94.850000    84.867500    89.807500  109725.750000   \n50%      99.690000   104.795000    94.790000    99.730000  139804.000000   \n75%     109.860000   114.810000   104.912500   110.020000  170379.250000   \nmax     120.000000   129.480000   119.520000   128.980000  199990.000000   \n\n                 市值          涨跌幅          换手率  \ncount  5.000000e+03  5000.000000  5000.000000  \nmean   1.498302e+06    -0.000498     0.124952  \nstd    4.063535e+05     0.058042     0.043443  \nmin    8.001240e+05    -0.100000     0.050000  \n25%    1.143199e+06    -0.050000     0.090000  \n50%    1.510834e+06     0.000000     0.130000  \n75%    1.846718e+06     0.050000     0.160000  \nmax    2.199679e+06     0.100000     0.200000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>收盘价</th>\n      <th>最高价</th>\n      <th>最低价</th>\n      <th>开盘价</th>\n      <th>成交量</th>\n      <th>市值</th>\n      <th>涨跌幅</th>\n      <th>换手率</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5000.000000</td>\n      <td>5000.000000</td>\n      <td>5000.000000</td>\n      <td>5000.000000</td>\n      <td>5000.000000</td>\n      <td>5.000000e+03</td>\n      <td>5000.000000</td>\n      <td>5000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>99.864676</td>\n      <td>104.904968</td>\n      <td>94.891296</td>\n      <td>99.882582</td>\n      <td>140136.678200</td>\n      <td>1.498302e+06</td>\n      <td>-0.000498</td>\n      <td>0.124952</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>11.578104</td>\n      <td>11.968002</td>\n      <td>11.978240</td>\n      <td>12.262939</td>\n      <td>34672.984375</td>\n      <td>4.063535e+05</td>\n      <td>0.058042</td>\n      <td>0.043443</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>80.000000</td>\n      <td>80.710000</td>\n      <td>70.140000</td>\n      <td>71.510000</td>\n      <td>80021.000000</td>\n      <td>8.001240e+05</td>\n      <td>-0.100000</td>\n      <td>0.050000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>89.750000</td>\n      <td>94.850000</td>\n      <td>84.867500</td>\n      <td>89.807500</td>\n      <td>109725.750000</td>\n      <td>1.143199e+06</td>\n      <td>-0.050000</td>\n      <td>0.090000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>99.690000</td>\n      <td>104.795000</td>\n      <td>94.790000</td>\n      <td>99.730000</td>\n      <td>139804.000000</td>\n      <td>1.510834e+06</td>\n      <td>0.000000</td>\n      <td>0.130000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>109.860000</td>\n      <td>114.810000</td>\n      <td>104.912500</td>\n      <td>110.020000</td>\n      <td>170379.250000</td>\n      <td>1.846718e+06</td>\n      <td>0.050000</td>\n      <td>0.160000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>120.000000</td>\n      <td>129.480000</td>\n      <td>119.520000</td>\n      <td>128.980000</td>\n      <td>199990.000000</td>\n      <td>2.199679e+06</td>\n      <td>0.100000</td>\n      <td>0.200000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "日期      0\n收盘价     0\n最高价     0\n最低价     0\n开盘价     0\n成交量     0\n市值      0\n涨跌幅     0\n换手率     0\n股票代码    0\n行业      0\ndtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().any()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "              日期     收盘价     最高价     最低价     开盘价     成交量       市值   涨跌幅   换手率  \\\n0     2026-01-15   94.53  104.17   92.13   98.65  190228  1759489  0.06  0.13   \n1     2024-10-21  103.14  107.63   98.66  101.41  125858  1979626  0.07  0.18   \n2     2026-02-23  118.96  123.81  110.71  111.84  123261  1747556 -0.02  0.06   \n3     2025-08-26   94.90   98.46   88.06   97.48  116065  2087308 -0.06  0.05   \n4     2024-07-23  102.80  110.39   96.88  102.59  109950  2120478 -0.09  0.18   \n...          ...     ...     ...     ...     ...     ...      ...   ...   ...   \n4995  2024-09-06   81.01   83.46   72.61   75.25   80381  2064695 -0.10  0.09   \n4996  2026-04-28  113.68  116.59  111.07  115.94   95991  1579650  0.06  0.06   \n4997  2026-04-12   91.75   96.90   89.26   92.13  186571  1550355 -0.01  0.13   \n4998  2024-11-26  112.27  114.89  104.20  107.91  182485   930311  0.09  0.09   \n4999  2025-07-19   93.60  102.36   84.24   91.71  136610  1989741  0.07  0.11   \n\n      股票代码  行业  \n0     A001  科技  \n1     A001  金融  \n2     A001  金融  \n3     A002  金融  \n4     A001  金融  \n...    ...  ..  \n4995  A001  金融  \n4996  A001  科技  \n4997  A002  金融  \n4998  A002  科技  \n4999  A002  金融  \n\n[5000 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>日期</th>\n      <th>收盘价</th>\n      <th>最高价</th>\n      <th>最低价</th>\n      <th>开盘价</th>\n      <th>成交量</th>\n      <th>市值</th>\n      <th>涨跌幅</th>\n      <th>换手率</th>\n      <th>股票代码</th>\n      <th>行业</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2026-01-15</td>\n      <td>94.53</td>\n      <td>104.17</td>\n      <td>92.13</td>\n      <td>98.65</td>\n      <td>190228</td>\n      <td>1759489</td>\n      <td>0.06</td>\n      <td>0.13</td>\n      <td>A001</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2024-10-21</td>\n      <td>103.14</td>\n      <td>107.63</td>\n      <td>98.66</td>\n      <td>101.41</td>\n      <td>125858</td>\n      <td>1979626</td>\n      <td>0.07</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2026-02-23</td>\n      <td>118.96</td>\n      <td>123.81</td>\n      <td>110.71</td>\n      <td>111.84</td>\n      <td>123261</td>\n      <td>1747556</td>\n      <td>-0.02</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2025-08-26</td>\n      <td>94.90</td>\n      <td>98.46</td>\n      <td>88.06</td>\n      <td>97.48</td>\n      <td>116065</td>\n      <td>2087308</td>\n      <td>-0.06</td>\n      <td>0.05</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2024-07-23</td>\n      <td>102.80</td>\n      <td>110.39</td>\n      <td>96.88</td>\n      <td>102.59</td>\n      <td>109950</td>\n      <td>2120478</td>\n      <td>-0.09</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4995</th>\n      <td>2024-09-06</td>\n      <td>81.01</td>\n      <td>83.46</td>\n      <td>72.61</td>\n      <td>75.25</td>\n      <td>80381</td>\n      <td>2064695</td>\n      <td>-0.10</td>\n      <td>0.09</td>\n      <td>A001</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4996</th>\n      <td>2026-04-28</td>\n      <td>113.68</td>\n      <td>116.59</td>\n      <td>111.07</td>\n      <td>115.94</td>\n      <td>95991</td>\n      <td>1579650</td>\n      <td>0.06</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>4997</th>\n      <td>2026-04-12</td>\n      <td>91.75</td>\n      <td>96.90</td>\n      <td>89.26</td>\n      <td>92.13</td>\n      <td>186571</td>\n      <td>1550355</td>\n      <td>-0.01</td>\n      <td>0.13</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n    <tr>\n      <th>4998</th>\n      <td>2024-11-26</td>\n      <td>112.27</td>\n      <td>114.89</td>\n      <td>104.20</td>\n      <td>107.91</td>\n      <td>182485</td>\n      <td>930311</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>A002</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>4999</th>\n      <td>2025-07-19</td>\n      <td>93.60</td>\n      <td>102.36</td>\n      <td>84.24</td>\n      <td>91.71</td>\n      <td>136610</td>\n      <td>1989741</td>\n      <td>0.07</td>\n      <td>0.11</td>\n      <td>A002</td>\n      <td>金融</td>\n    </tr>\n  </tbody>\n</table>\n<p>5000 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()\n",
    "df.drop_duplicates()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "              日期     收盘价     最高价     最低价     开盘价     成交量       市值   涨跌幅   换手率  \\\n0     2026-01-15   94.53  104.17   92.13   98.65  190228  1759489  0.06  0.13   \n1     2024-10-21  103.14  107.63   98.66  101.41  125858  1979626  0.07  0.18   \n2     2026-02-23  118.96  123.81  110.71  111.84  123261  1747556 -0.02  0.06   \n3     2025-08-26   94.90   98.46   88.06   97.48  116065  2087308 -0.06  0.05   \n4     2024-07-23  102.80  110.39   96.88  102.59  109950  2120478 -0.09  0.18   \n...          ...     ...     ...     ...     ...     ...      ...   ...   ...   \n4995  2024-09-06   81.01   83.46   72.61   75.25   80381  2064695 -0.10  0.09   \n4996  2026-04-28  113.68  116.59  111.07  115.94   95991  1579650  0.06  0.06   \n4997  2026-04-12   91.75   96.90   89.26   92.13  186571  1550355 -0.01  0.13   \n4998  2024-11-26  112.27  114.89  104.20  107.91  182485   930311  0.09  0.09   \n4999  2025-07-19   93.60  102.36   84.24   91.71  136610  1989741  0.07  0.11   \n\n      股票代码  行业  \n0     A001   1  \n1     A001   0  \n2     A001   0  \n3     A002   0  \n4     A001   0  \n...    ...  ..  \n4995  A001   0  \n4996  A001   1  \n4997  A002   0  \n4998  A002   1  \n4999  A002   0  \n\n[5000 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>日期</th>\n      <th>收盘价</th>\n      <th>最高价</th>\n      <th>最低价</th>\n      <th>开盘价</th>\n      <th>成交量</th>\n      <th>市值</th>\n      <th>涨跌幅</th>\n      <th>换手率</th>\n      <th>股票代码</th>\n      <th>行业</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2026-01-15</td>\n      <td>94.53</td>\n      <td>104.17</td>\n      <td>92.13</td>\n      <td>98.65</td>\n      <td>190228</td>\n      <td>1759489</td>\n      <td>0.06</td>\n      <td>0.13</td>\n      <td>A001</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2024-10-21</td>\n      <td>103.14</td>\n      <td>107.63</td>\n      <td>98.66</td>\n      <td>101.41</td>\n      <td>125858</td>\n      <td>1979626</td>\n      <td>0.07</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2026-02-23</td>\n      <td>118.96</td>\n      <td>123.81</td>\n      <td>110.71</td>\n      <td>111.84</td>\n      <td>123261</td>\n      <td>1747556</td>\n      <td>-0.02</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2025-08-26</td>\n      <td>94.90</td>\n      <td>98.46</td>\n      <td>88.06</td>\n      <td>97.48</td>\n      <td>116065</td>\n      <td>2087308</td>\n      <td>-0.06</td>\n      <td>0.05</td>\n      <td>A002</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2024-07-23</td>\n      <td>102.80</td>\n      <td>110.39</td>\n      <td>96.88</td>\n      <td>102.59</td>\n      <td>109950</td>\n      <td>2120478</td>\n      <td>-0.09</td>\n      <td>0.18</td>\n      <td>A001</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4995</th>\n      <td>2024-09-06</td>\n      <td>81.01</td>\n      <td>83.46</td>\n      <td>72.61</td>\n      <td>75.25</td>\n      <td>80381</td>\n      <td>2064695</td>\n      <td>-0.10</td>\n      <td>0.09</td>\n      <td>A001</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4996</th>\n      <td>2026-04-28</td>\n      <td>113.68</td>\n      <td>116.59</td>\n      <td>111.07</td>\n      <td>115.94</td>\n      <td>95991</td>\n      <td>1579650</td>\n      <td>0.06</td>\n      <td>0.06</td>\n      <td>A001</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4997</th>\n      <td>2026-04-12</td>\n      <td>91.75</td>\n      <td>96.90</td>\n      <td>89.26</td>\n      <td>92.13</td>\n      <td>186571</td>\n      <td>1550355</td>\n      <td>-0.01</td>\n      <td>0.13</td>\n      <td>A002</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4998</th>\n      <td>2024-11-26</td>\n      <td>112.27</td>\n      <td>114.89</td>\n      <td>104.20</td>\n      <td>107.91</td>\n      <td>182485</td>\n      <td>930311</td>\n      <td>0.09</td>\n      <td>0.09</td>\n      <td>A002</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4999</th>\n      <td>2025-07-19</td>\n      <td>93.60</td>\n      <td>102.36</td>\n      <td>84.24</td>\n      <td>91.71</td>\n      <td>136610</td>\n      <td>1989741</td>\n      <td>0.07</td>\n      <td>0.11</td>\n      <td>A002</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5000 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"行业\"].replace({\"金融\":0,\"科技\":1},inplace=True)\n",
    "X = df.iloc[:,1:-2]\n",
    "y = df[\"行业\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[-0.46080167, -0.06141723, -0.23054908, ...,  0.64282343,\n         1.04241039,  0.11621106],\n       [ 0.28291779,  0.22771592,  0.31466066, ...,  1.18461534,\n         1.21471532,  1.2672715 ],\n       [ 1.64942673,  1.57978943,  1.32075214, ...,  0.61345444,\n        -0.33602908, -1.49527356],\n       ...,\n       [-0.70093409, -0.6689311 , -0.47017419, ...,  0.12811165,\n        -0.16372415,  0.11621106],\n       [ 1.07155412,  0.83439415,  0.77721227, ..., -1.39791452,\n         1.55932519, -0.80463729],\n       [-0.54113374, -0.21266896, -0.88930939, ...,  1.20950995,\n         1.21471532, -0.34421311]])"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "transfer = StandardScaler()\n",
    "X = transfer.fit_transform(X)\n",
    "X"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "# pip install seaborn\n",
    "sns.heatmap(df.corr(),annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.96876362,  1.07923811,  1.09198114, ...,  0.42078515,\n         0.35319065, -1.03484938],\n       [ 1.63646995,  1.33243853,  1.33244117, ..., -0.68110967,\n        -0.50833401,  1.49748359],\n       [-1.22266064, -0.77422236, -1.17986527, ..., -1.38774747,\n         1.21471532,  1.49748359],\n       ...,\n       [-0.69488759, -0.32798796, -0.62046171, ...,  1.45359774,\n        -1.36985868, -1.03484938],\n       [ 0.13348287,  0.46921731,  0.0299493 , ..., -0.93045942,\n        -0.85294388,  1.03705942],\n       [ 1.00763398,  0.55612438,  0.8515211 , ...,  0.5022987 ,\n         1.55932519,  1.49748359]])"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier()",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "SVC(C=1, kernel='poly')",
      "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_poly  = SVC(kernel='poly',C=1)\n",
    "svm_poly.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,mean_squared_error,recall_score,roc_auc_score\n",
    "\n",
    "y_rf_pred = rf.predict(X_test)\n",
    "y_svm_pred = svm_poly.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "0.491"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_rf_pred)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "0.503"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_svm_pred)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "     a\n0    0\n1    0\n2    1\n3    0\n4    1\n..  ..\n995  0\n996  0\n997  0\n998  1\n999  0\n\n[1000 rows x 1 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "res = DataFrame({\"a\":y_svm_pred})\n",
    "res"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "1000"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sqlalchemy import create_engine\n",
    "conn = create_engine('mysql+pymysql://root:123456@localhost/db_16')\n",
    "res.to_sql(\"res\",conn)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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